By: Imogen Cleaver-Stigum, Andrew Nolan, Matthew St Louis, Jyalu Wu
This is the repository for our CS 573 final project. We conducted experiment regarding weather uncertainty visualizations. You can read more about it in our Process Book!
Tableau Visualizations of Experiment Results
The Process Book PDF is in the repo!
There is a lot of content in our final project submission. This is a brief overview of those files:
- ProcessBook.pdf is our ProcessBook
- data-analysis: This folder includes our final survey results data and code for how we analyzed it (a python notebook and a tableau project).
- data: This include the raw json results of our survey
- dotplot: Includes some early code from trying to generate a quantile dot plot.
- react-firebase: This is where most of our code lives including the survey website and the visualizations.
- The src folder contains: App.js, ExitSurvey.js, Instructions.js, Question.js, and Survey.js. These are all React components used to generate our survey website
- The src folder also contains: bar-charts.js, d3-hops.js, dotplot.js, and data-generation.js. These files handle generating our weather data and the visualizations
- M. Kay, T. Kola, J. R. Hullman, and S. A. Munson, “When (ish) is my bus? user-centered visualizations of uncertainty in everyday, mobile predictive systems,” in Proceedings of the 2016 chi conference on human factors in computing systems, 2016, pp. 5092–5103.
- A. Kale, F. Nguyen, M. Kay, and J. Hullman, “Hypothetical outcome plots help untrained observers judge trends in ambiguous data,” IEEE transactions on visualization and computer graphics, vol. 25, no. 1, pp. 892–902, 2018.
- M. Correll, D. Moritz, and J. Heer, “Value-suppressing uncertainty palettes,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 2018, pp. 1–11.
- J. Sanyal, S. Zhang, J. Dyer, A. Mercer, P. Amburn, and R. Moorhead, “Noodles: A tool for visualization of numerical weather model ensemble uncertainty,” IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1421–1430, 2010.
- L. Nadav-Greenberg, S. L. Joslyn, and M. U. Taing, “The effect of uncertainty visualizations on decision making in weather forecasting,” Journal of Cognitive Engineering and Decision Making, vol. 2, no. 1, pp. 24–47, 2008.
- A. Kale, M. Kay, and J. Hullman, “Visual reasoning strategies and satisficing: How uncertainty visualization design impacts effect size judgments and decisions,” arXiv preprint arXiv:2007.14516, 2020.
- J. Hullman, X. Qiao, M. Correll, A. Kale, and M. Kay, “In pursuit of error: A survey of uncertainty visualization evaluation,” IEEE transactions on visualization and computer graphics, vol. 25, no. 1, pp. 903–913, 2018.
- J. Hullman, "Why Authors Don't Visualize Uncertainty," in IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 130-139, Jan. 2020, doi: 10.1109/TVCG.2019.2934287
- Weather Shack, “Rain Measurement,” 2021, https://www.weathershack.com/static/ed-rain-measurement.html
- OpenWeather API, 2021, https://openweathermap.org/api
- J. Evans, “Creating a Production Build”, 2019, https://create-react-app.dev/docs/production-build/
- AV Dojo, “React and Firebase | Firebase Realtime database with React |”, 2019, https://www.youtube.com/watch?v=0pC8dEqSKkc
- J. Richards, “How to Deploy a React App with Firebase Hosting”, 2019, https://medium.com/swlh/how-to-deploy-a-react-app-with-firebase-hosting-98063c5bf425
- Vega, “Quantile Dot Plot Example”, https://vega.github.io/vega/examples/quantile-dot-plot/
- M. Kay, “Quantile dotplots”, 2016, https://github.com/mjskay/when-ish-is-my-bus/blob/master/quantile-dotplots.md